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A major factor hindering the deployment of a fully functional automatic facial expression detection system is the lack of representative data. A solution to this is to narrow the context of the target application, so enough data is available to build robust models so high performance can be gained. Automatic pain detection from a patient's face represents one such application. To facilitate this work,...
Object instance matching is a cornerstone component in many computer vision applications such as image search, augmented reality and unsupervised tagging. The common flow in these applications is to take an input image and match it against a database of previously enrolled images of objects of interest. This is usually difficult as one needs to capture an image corresponding to an object view already...
This paper presents a new extended collection of posed and induced facial expression image sequences. All sequences were captured in a controlled laboratory environment with high resolution and no occlusions. The collection consists of two parts: The first part depicts eighty six subjects performing the six basic expressions according to the “emotion prototypes” as defined in the Investigator's Guide...
We present a bimodal information analysis system for automatic emotion recognition. Our approach is based on the analysis of video sequences which combines facial expressions observed visually with acoustic features to automatically recognize five universal emotion classes: anger, disgust, happiness, sadness and surprise. We address the challenges posed during the temporal analysis of the bimodal...
Studies on face aging are handicapped by lack of long term dense aging sequences for model training. To handle this problem, we propose a new face aging model, which learns long term face aging patterns from partially dense aging databases. The learning strategy is based on two assumptions: (i) short term face aging pattern is relatively simple and is possible to be learned from currently available...
In this paper we present a system of the off-line handwriting recognition. Our recognition system is based on temporal order restoration of the off-line trajectory. For this task we use a genetic algorithm (GA) to optimize the sequences of handwritten strokes. To benefit from dynamic informations we make a sampling operation by the consideration of trajectory curvatures. We proceed to calculate the...
We propose a new error concealment method based on hallucination for Scalable Video Coding with spatial scalability. In this method, parts of the frames which lose the enhancement layer are up-sampled from base layer and ldquohallucinatedrdquo as concealment frames. The database for hallucination is generated from the high-resolution and low-resolution frame-pairs near the lost frames in the video...
Graphical models have proved to be very efficient models for labeling image data. In particular, they have been used to label data samples from human body images. In this paper, a DTG-based graphical model is studied for human-body landmark localization and tracking along the image sequence. Experimental results on human motion databases are shown.
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-epsiv-join over two time series. Given two time series,...
In this paper, our proposed structured human motion database is adopted for different motion representations. The motions are first represented as a sequence of frames of 2D images, which were compressed using three recognized motion representation techniques: exclusive-OR, MEI (motion energy image), and MHI (motion history images). The representation is a 2D feature image. The feature image is compressed...
This paper proposes a framework for retrieving semantic video events from indoor surveillance video databases. The goal is to locate video sequences containing events of interest to the user. This framework starts by tracking objects and segmenting videos into Common Appearance Intervals (CAIs). The spatiotemporal trajectories are obtained, based on which features are extracted for the construction...
This paper presents an unsupervised learning approach to video-based face recognition that does not make any assumptions about the pose, expressions or prior localization of landmarks on the faces. The proposed algorithm exploits spatiotemporal information obtained from local features that are extracted from arbitrary keypoints on faces as opposed to pre-defined landmarks. The algorithm is inherently...
Face information processing relies on the quality of data resource. From the data modality point of view, a face database can be 2D or 3D, and static or dynamic. From the task point of view, the data can be used for research of computer based automatic face recognition, face expression recognition, face detection, or cognitive and psychological investigation. With the advancement of 3D imaging technologies,...
Face recognition finds its place in a large number of applications. They occur in different contexts related to security, entertainment or Internet applications. Reliable face recognition is still a great challenge to computer vision and pattern recognition researchers, and new algorithms need to be evaluated on relevant databases. The publicly available IV2 database allows monomodal and multimodal...
With this enormous speed in generating and collecting images, there is an extreme need in extracting interesting and useful knowledge from image archives. In our previous works, we have proposed an image mining framework to extract knowledge from a sequence of images. The framework is composed of two main modules: image analysis and knowledge processing. In this paper, we successfully customized the...
Obtaining ground-truth motion for arbitrary, real-world video sequences is a challenging but important task for both algorithm evaluation and model design. Existing ground-truth databases are either synthetic, such as the Yosemite sequence, or limited to indoor, experimental setups, such as the database developed by Baker et al (2007). We propose a human-in-loop methodology to create a ground-truth...
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